Human factors implications of vehicle automation: Current understanding and future directions

Merat, Natasha; de Waard, Dick · 2014 · Crossref

DOI: 10.1016/j.trf.2014.11.002

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Summary

This guest editorial introduces a special issue of *Transportation Research Part F* focused on the human factors implications of vehicle automation. The authors, Merat and de Waard, contextualize the research within the rapid advancement of autonomous vehicle technologies by automotive manufacturers and service providers, noting that while trials are underway, homogeneous global implementation remains distant. The issue aims to synthesize empirical findings on driver interactions with highly or fully automated driving (HAD/FAD) systems, moving beyond isolated studies of single support systems like Adaptive Cruise Control (ACC) to examine complex interactions and mixed-traffic scenarios. The summary reviews several key studies included in the special issue. De Winter et al. (2014) conducted a meta-analysis of 30 studies, finding that while HAD reduces driver workload compared to manual driving or ACC, it leads to increased engagement in non-driving related tasks. Strand et al. (2014) utilized a motion-based simulator to assess driver responses to automation failures, introducing the “point-of-no-return” metric. They found that increasing automation levels reduced situation awareness, evidenced by more frequent points of no return and reduced minimum Time To Collision. Larsson et al. (2014) examined driver experience, reporting that ACC users exhibited slower brake reaction times to cut-in events than manual drivers, though familiarity with the system improved response speeds, highlighting the importance of long-term adaptation. Schieben et al. (2014) evaluated automatic steering interventions for collision avoidance, finding that while pure steering interventions were safer than manual braking, drivers often interfered with the system, and additional auditory or haptic signals did not significantly improve collision avoidance rates. Further contributions address public acceptance and mixed-traffic dynamics. Payre et al. (2014) surveyed 421 French drivers, revealing a 68% willingness to accept fully automated vehicles, contrasting sharply with an 18% acceptance rate among British drivers; men and high sensation-seekers were more favorable. Gouy et al. (2014) demonstrated that drivers of non-automated vehicles adopt platoon-like behavior, reducing time headways when driving near automated platoons. Merat et al. (2014) found that resuming control from automation requires approximately 40 seconds for performance to stabilize, with erratic vehicle management occurring in the first 10–15 seconds. The authors conclude that while these studies provide robust insights, significant gaps remain. Future research must prioritize the long-term effects of automation on driver adaptation, as current studies often focus on initial encounters. Additionally, there is a critical need to investigate the interaction between automation and drivers of varying ages, particularly older adults whose cognitive performance declines, as well as young novice drivers. Understanding these demographic-specific interactions is essential for optimizing support systems and ensuring road safety in an era of mixed automated and manual traffic.

Key finding

Highly automated driving reduces driver workload but increases non-driving task engagement and can impair situation awareness, while driver experience and prolonged adaptation are critical for safe interaction with automated systems.

Methodology

review

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• automation
• automation surprise
• situational awareness
• acceptance adoption
• automation complacency bias
• takeover transitions

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: behavioral performance data
• Theoretical Contribution: conceptual framework, theory or model